The ultimate guide to data science interviews. Developed by FAANG engineers, it covers Python, algorithms, ML concepts, and interview questions. Get interview-ready in just a few hours.

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This course will increase your skills to crack the data science or machine learning interview. You will cover all the most common data science and ML concepts coupled with relevant interview questions.
You will start by covering Python basics as well as the most widely used algorithms and data structures. From there, you will move on to more advanced topics like feature engineering, unsupervised learning, as well as neural networks and deep learning.
This course takes a non-traditional approach to interview prep, in that it focuses on data science fundamentals instead of open-ended questions.

In all, this course will get you ready for data science interviews. By the time you finish this course, you will have reviewed all the major concepts in data science and will have a good idea of what interview questions you can expect.

Data Science Interview Handbook

When we build the ML model, we use the data and create the model. Consider the problem of predicting the skill level of a student from his answers to a different question. We take the data and create a classification model that tells us the skill of a student on a scale of 1 to 5 (integers). We verify this model on the data available to build the model. 

However, what happens when we deploy this model to production? Maybe it is good for some students, but it gives bad results for others. Maybe some highly skilled students got lower ratings due to wrong answers to a particular question. So, we may end up either creating a good model or a bad model that no one wants to use. Our goal is to always create the best model. In cases where business depends on ML models, it is necessary to create a reliable, good model.




<h2>Loss function</h2>

So, we want to know how successful the model is or the amount of loss associated with our predictions. This can be measured with a loss function.

> Loss function = L(y,fw(x))

Here, y stands for the true value, and fw(x) is the predicted value. L is the function that takes these two and gives the loss using some computation.

For example 

> Squared error function: (y - fw(x))2

>Absolute error function: |y - fw(x)|

Learn how to measure machine learning model performance. This lesson discusses training error, testing error, overfitting, underfitting, bias, variance, and dataset splitting. 

Are You Ready to Become a Data Scientist?

Python Basics

Python Libraries

More Data Science Tools

Data Structures and Algorithms - I

Data Structures and Algorithms - II

Statistics and Probability

Feature Engineering

Basics of Machine Learning

Regression

Classification

Unsupervised Learning

Advanced Topics in Machine Learning

Conclusion

Measuring ML Model Performance

Loss function